Specific networks of plasma acute phase reactants are associated with the severity of chronic obstructive pulmonary disease: A case-control study

A detailed understanding of the intricate relationships between different acute phase reactants (APRs) in chronic obstructive pulmonary disease (COPD) can shed new light on its clinical course. In this case-control study, we sought to identify the interaction networks of a number of plasma APRs in COPD, with a special focus on their association with disease severity.

International Journal of Medical Sciences

2017; 14(1): 67-74 doi: 10.7150/ijms.16907

Research Paper

Specific networks of plasma acute phase reactants are associated with the severity of chronic obstructive

pulmonary disease: a case-control study

Elena Arellano-Orden1 , Carmen Calero-Acuña1, 2,3, Juan Antonio Cordero1, María Abad-Arranz2,

Verónica Sánchez-López1, Eduardo Márquez-Martín1, 2, Francisco Ortega-Ruiz1,2,3, José Luis

López-Campos1,2,3

1 Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/Universidad de Sevilla, Seville, Spain;

2 Unidad Médico-Quirúrgica de Enfermedades Respiratorias, Hospital Virgen del Rocío Seville, Spain;

3 CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain

 Corresponding author: Elena Arellano-Orden, Instituto de Biomedicina de Sevilla (IBiS), Avda Manuel Siurot, s/n.41013 Seville, Spain E-mail: marellano-ibis@us.es

© Ivyspring International Publisher This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/) See http://ivyspring.com/terms for full terms and conditions

Received: 2016.07.20; Accepted: 2016.11.01; Published: 2017.01.15

Abstract

Objectives A detailed understanding of the intricate relationships between different acute phase

reactants (APRs) in chronic obstructive pulmonary disease (COPD) can shed new light on its

clinical course In this case-control study, we sought to identify the interaction networks of a

number of plasma APRs in COPD, with a special focus on their association with disease severity

Methods COPD cases and healthy smoking controls (3:1 ratio) were recruited in our outpatient

pulmonary clinic Cardiopulmonary exercise testing was used to rule out the presence of ischemic

heart disease All subjects were males as per protocol Multiple plasma APRs – including

α-2-macroglobulin, C-reactive protein (CRP), ferritin, fibrinogen, haptoglobin, procalcitonin

(PCT), serum amyloid A (SAA), serum amyloid P, and tissue plasminogen activator (tPA) – were

measured using commercial Acute Phase Bio-Plex Pro Assays and analyzed on the Bio-Plex

manager software Correlations between different APRs were investigated using a heat map

Network visualization and analyses were performed with the Cytoscape software platform

Results A total of 96 COPD cases and 33 controls were included in the study Plasma A2M, CRP,

and SAP levels were higher in COPD patients than in controls Circulating concentrations of

haptoglobin and tPA were found to increase in parallel with the severity of the disease Increasing

disease severity was associated with distinct intricate networks of APRs, which were especially

evident in advanced stages

Conclusions We identified different networks of APRs in COPD, which were significantly

associated with disease severity

Key words: acute phase reactants, chronic obstructive pulmonary disease

Introduction

Recent years have witnessed an increasing

interest in the occurrence of systemic inflammation in

COPD – which can explain, at least in part, its main

extrapulmonary manifestations (1, 2) In general, the

term systemic inflammation indicates an increase in

plasma levels of various inflammatory proteins and

acute phase reactants (APRs) belonging to different

biological pathways An elevation in circulating inflammatory markers may represent a potential therapeutic target (3, 4) for tackling the systemic burden of COPD (5), with several studies showing a significant adverse prognostic significance of increased APRs levels (6-8)

In all mammalian species, APRs are released Ivyspring

International Publisher

from the liver to the systemic circulation mainly

through the action of different proinflammatory

cytokines (e.g., IL-6, IL-1 and TNF-α) Two distinct

APRs classes can be distinguished based on the

expression patterns elicited by cytokines on the liver

Class 1 APRs – which are mainly regulated by IL-1 or

the combination of IL-1, IL-6, and glucocorticoids –

include haptoglobin, C-reactive protein (CRP), serum

amyloid A (SAA), α-1 acid glycoprotein (AGP), and

hemopexin Class 2 APRs – which are solely regulated

by IL-6 and glucocorticoids – consist of fibrinogen, α-1

antichymotrypsin, and α-1 antitrypsin (9)

The most important research gaps that currently

exist in the field of systemic inflammation in COPD

include a) the exact source of APRs release, b) the

potential interindividual variability of the

inflammatory response, and c) how distinct

inflammatory biomarkers can drive disease

progression (4) Moreover, the observation that

different APRs are not elevated in isolation supports

the existence of intricate networks of different

proinflammatory molecules that can fine-tune the

systemic manifestations of COPD (10) Although there

is evidence that combining information from different

APRs may improve the prediction of progression in

COPD, most studies to date have focused only on a

limited number of different inflammatory biomarkers

(10) Another major issue of the available

investigations is the potential confounding effect of

ischemic heart disease, which is a common

comorbidity associated with systemic inflammation

as well (11)

The identification of specific inflammatory

signatures that may reflect disease severity in COPD

is paramount for risk stratification and can shed new

light on the disease course To this aim, we designed

the current study to perform an integrative analysis of

different APRs Specifically, the network visualization

approach used in this report enabled us to obtain an

overview of the complex relationships between

different inflammatory markers, with a special focus

on their association with disease severity Owing to a

potential confounding effect, female subjects and

patients with ischemic heart disease were excluded

from this study

Methods

Study design and participants

This case-control study was conducted at the

University Hospital Virgen del Rocío (Seville, Spain)

between 2007 and 2010 Ethical approval was granted

by the local Institutional Review Board (Comité de

Ética e Investigación Clínica del Hospital Virgen del

Rocío, Seville, Spain; approval act: 02/2006) Written

informed consent was obtained from all participants All analyses were performed in a cross-sectional fashion COPD cases and healthy smoking controls (3:1 ratio) were recruited in our outpatient pulmonary clinic Only male subjects were included to avoid the confounding effect of sex distribution Inclusion criteria for COPD cases were as follows: 1) smokers and former-smokers with a diagnosis of COPD and a post-bronchodilator forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) ratio <0.7, 2) negative history of acute exacerbations in the previous three months, and 3) male sex Smokers and former-smokers aged > 40 years with an FEV1/FVC ratio ≥ 0.7 were deemed eligible as controls The measurement of exhaled carbon monoxide was used for confirming the smoking status in all participants Exclusion criteria for both cases and controls included

a previous history of ischemic heart disease, congestive heart failure, ventilator dependency, malignancies, hepatic cirrhosis, end-stage renal disease, rheumatologic disorders, tuberculosis, orthopedic conditions (that precluded or limited the performance in the walking and cardiopulmonary exercise tests), neurological or psychiatric illnesses that could interfere with the participation in the study, or any systemic inflammatory or infectious disease that could be associated with increased APRs levels All participants underwent a cardiopulmonary exercise test coupling ECG with metabolic changes together with the clinical history and physical examination to rule out the presence of ischemic heart disease In presence of positive results, the subject was excluded from the study and referred to the cardiology department for appropriate care

Laboratory methods

Blood samples were drawn by venipuncture from each subject at rest Samples were centrifuged at

3000 rpm for 5 min and stored at -80 °C until assayed Plasma α-2-macroglobulin (A2M), C-reactive protein (CRP), ferritin, fibrinogen, haptoglobin, procalcitonin (PCT), serum amyloid A (SAA), serum amyloid P (SAP), and tissue plasminogen activator (tPA) concentrations were measured using commercially available Acute Phase Bio-Plex Pro Assays (BioRad Laboratories; Hercules, CA, USA) according to the manufacturer’s protocol The assay working ranges (defined by the ranges that extended from the lower

to the upper limits of quantification) were as follows: 0.5−1875 ng/mL for A2M, 0.01−50 ng/mL for CRP, 3.05−50000 pg/mL for ferritin, 5−813 ng/mL for fibrinogen, 0.1−500 ng/mL for haptoglobin, 14−10000 pg/mL for PTC, 1−700 ng/mL for SAA, 0.1−200 ng/mL for SAP, and 28−5.000 pg/mL for tPA All samples were blinded by a numerical code and

laboratory personnel were unaware of the

case-control status of each specimen Measurements

were performed in random order All samples were

analyzed in duplicate and the mean of the two

measures was used for analysis Plasma specimens

(final volume: 50 μL) were diluted 100-fold for the

measurements of A2M, CRP, ferritin, fibrinogen, and

haptoglobin, whereas 10000-fold-diluted aliquots

were used for quantifying PCT, SAA, SAP, and tPA

The analytical platform consisted of a 96-well

plate-formatted, bead-based assay, with specific

antibodies directed against the target proteins

covalently coupled to the surfaces of the internally

dyed bead sets After a series of washing steps to

remove unbound proteins, a biotinylated detection

antibody specific for each epitope was added to the

reaction The beads were subsequently incubated with

a reporter streptavidin-phycoerythrin (SA-PE)

conjugate, and fluorescence of the bound SA-PE was

measured through the specific array reader

Data acquisition and analysis

All analytical data were acquired using the

Bio-Plex platform (Bio-Rad Laboratories, Hercules,

CA, USA), consisting of a suspension array system, a

dual laser, and a flow-based microplated reader The

laser and associated optics are designed to detect the

internal fluorescence of the individual dyed beads

The fluorescent signal on the bead surface is

proportional to the quantity of target protein in the

biological sample All of the data were analyzed on

the Bio-Plex manager software

Statistical analysis

All calculations were conducted in the

computing environment R (version 3.3.0; R

Foundation for Statistical Computing, Vienna,

Austria) Data pre-processing was performed by

log-transformation and removal of out-of-range

outliers For each APR, we also considered as outliers

all of the measures that fell outside the interval

comprised between the first quartile minus two times

the interquartile range (IQR) and the third quartile

plus two times the IQR Because data had a skewed

distribution according to the Shapiro-Wilk test

(p-value < 0.05), only non-parametric tests were

applied to compare distributions A network was

computed based on the pairwise correlations between

APRs Edges were present when the calculation of the

Spearman’s correlation coefficient identified a

statistically significant association (p-value < 0.05)

Edge width represented the absolute correlation

coefficient value, whereas color indicated the

presence of a negative (black) or a positive (grey)

association Node size was proportional to the APR

concentration The heatmaps denoted the similarities

in terms of biomarker profiles both in COPD patients and in control individuals Spearman’s correlation was considered as a similarity measure and numbers

in each cell were the p-values for every correlation between different APRs Because age was found to differ significantly between COPD patients and

control individuals (Mann-Whitney U test), the effect

of age on APRs levels was further tested using linear models Differences between COPD patients and healthy controls, as well as across different disease

stages, were calculated with the Mann-Whitney U test

Results

A total of 96 COPD cases and 33 controls were included in the study The general characteristics of the study participants are summarized in Table 1 The distribution of COPD stages was as follows: GOLD I,

23 patients (24%); GOLD II, 30 patients (31.2%); GOLD III, 28 patients (29.2%), and GOLD IV, 15 patients (15.6%)

Table 1 General characteristics of the study participants

Control subjects COPD patients p value* Males (n) 33 (100%) 96 (100%) NS Age (years) 58 (10) 67 (8) <0.001 Tobacco history (pack-years) 46.9 (27.8) 71.9 (76.6) 0.007 Body mass index (kg/m2) 28.78 (5) 28.27 (4.8) NS Charlson-age index 2.24 (1.6) 3.87 (1.2) <0.001 FVC (%) 91.59 (13.6) 91.96 (20.9) NS FEV1 (%) 90.26 (13.1) 59.15 (22.8) <0.001

Concentrations of acute phase reactants

There were not significant differences in the levels of A2M, CRP, ferritin, fibrinogen, haptoglobin, SAA and SAP between COPD patients and control subjects (Figure 1) However, plasma PCT and tPA levels were significantly higher in controls than in COPD patients (p=0.0211 and p=0.0434, respectively) Plasma levels of CRP, haptoglobin, PCT, and tPA were found to increase in parallel with disease severity (data not shown)

Associations between different biomarkers and identification of networks

Correlations between different APRs were summarized in a heat map (Figure 2) The most robust correlations in the control group were those between A2M and SAP (r=0.835, p<0.001), PCT and SAA (r=0.708, p=0.05), as well as PCT and tPA (r=0.877, p<0.001) Conversely, the most marked correlation in COPD patients was that between SAP and A2M (r=0.713, p<0.001) Consequently, we identified a cluster formed by SAP, A2M, haptoglobin, and CRP

in controls (Figure 2A) Two clusters were evident in

COPD patients, the first being between PCT, tPA, and SAA and the second between CRP, SAP, and A2M (Figure 2B)

Figure 1 Levels of A2M (panel A), CRP (panel B), Ferritin (panel C), Fibrinogen (panel D), Haptoglobin (panel E), PCT (panel F), SAA (panel G) and tPA (panel H)

in COPD cases and healthy smoking controls

Figure 2 Heat map depicting the correlations between di_erent in_ammatory biomarkers in healthy smoking controls (panel A) and COPD cases (panel B) The

intensity of the color re_ects the correlation coe_cient, whereas the number in each square indicates the p value

After network modelling (Figure 3), we were

able to identify distinct networks of inflammatory

biomarkers according to the presence and severity of

COPD In control subjects, there was a relation among

CRP, SAP, haptoglobin, and A2M, as well as another

association between tPA and PCT In COPD, we

found an additional correlation of SAA with all of

these networks We then analyzed the networks in

relation to GOLD functional stages (Figure 4) GOLD I

was characterized by a nonspecific increase in all of

the assayed APRs However, specific networks of

inflammatory markers were identified as the severity

of the disease increased The intermediate disease stages (GOLD II and III) were characterized by an increased extent of the associations between different APRs Interestingly, advanced disease stages (GOLD III-IV) showed inflammatory networks in which haptoglobin was independent from the cluster formed

by SAP, CRP, and A2M Finally, stage IV was characterized by a new cluster consisting of fibrinogen, PCT, and tPA

Figure 3 Distinct networks of acute phase reactants in healthy smoking

controls (panel A) and COPD patients (panel B) Node colors indicate the type

of correlation (grey and black denote positive and negative correlations,

respectively), whereas node size is proportional to the extent of correlation

Discussion

In this study conducted in male COPD patients

without comorbid ischemic heart disease, we

performed a comprehensive assessment of APRs by

network analysis As expected, APRs were found to

be increased in COPD More importantly, we

demonstrated that these biomarkers were reciprocally

interrelated, being associated in complex interactive

networks that were related to the severity of COPD

APRs represent a super-family of different

molecules whose circulating concentrations change by

at least 25% in response to acute adverse stimuli

Specifically, positive acute-phase proteins show a

significant increase during inflammation, whereas

negative acute-phase reactants are characterized by

corresponding reductions (12) Changes in APRs

concentrations are generally believed to reflect their synthesis rates in the hepatocytes However, the magnitude of inflammation-related APRs changes varies widely between different molecules For example, levels of ceruloplasmin and several components of the complement system may increase

by approximately 50 percent during inflammation, whereas up to 1000-fold increases have been reported for CRP and SAA (13)

In this study, we performed a number of APRs measurements to analyze the inflammatory networks

in COPD We uncovered intricate relationships between different inflammatory parameters in relation to the severity of the disease Moreover, we confirmed that COPD patients free of ischemic heart disease are characterized by a significant elevation in APRs compared with healthy smoking controls Of the different inflammatory markers, we observed a significant stepwise elevation in tPA and haptoglobin levels with increasing disease severity

Our results may pave the way for longitudinal investigations on the prognostic significance of the inflammatory signatures identified in our study using independent and larger sample sets (14) Additionally, the potential impact of the COPD clinical phenotype (15) on the inflammatory networks needs to be examined The question as to whether the markers or signatures herein identified could reflect subtle interindividual differences in the inflammatory expression of COPD remains open However, an important strength inherent in our study is the careful exclusion of patients with ischemic heart disease through cardiopulmonary exercise testing Because cardiovascular disorders are closely intertwined with COPD and can act as a major confounder, we deemed all patients with a positive exercise test not eligible for inclusion The relationship between COPD and ischemic heart disease is well-known (16) and both conditions share common risk factors, including age and tobacco smoke (17) In turn, the systemic inflammatory reaction occurring in COPD (18) may increase the risk of developing vascular manifestations (19, 20)

Published data on the concomitant changes of different inflammatory biomarkers in COPD are

scarce In the Evaluation of COPD Longitudinally to

Identify Predictive Surrogate End-points (ECLIPSE)

study (7), the authors measured a total of six inflammatory biomarkers (white blood cell count, CRP, interleukin [IL]-6, IL-8, fibrinogen, and tumor necrosis factor [TNF]-α) in three groups of subjects (1755 COPD patients, 297 smokers with normal results on spirometry, and 202 non-smokers) over a 3-year follow-up A persistent systemic inflammatory load was observed in a significant proportion of the

study patients Moreover, specific interplays between

different biomarkers were noted in COPD patients

compared with healthy smokers (i.e., increased white

blood cell count, CRP, IL-6, and fibrinogen

accompanied by a decreased expression of IL-8 and

TNF-α)

One of the most innovative aspects of our study

is the identification of specific networks of plasma

APRs as significantly associated with the severity of

COPD We identified a strong association between

CRP and SAP, which act as opsonins Conversely, we

did not find an association between SAA and CRP

Although these molecules share similar secretory

stimuli (13), CRP mainly activates the complement

system whereas SAA acts predominantly on

leucocytes Interestingly, we demonstrate for the first

time that fibrinogen is not part of the inflammatory

networks that characterize mild-to-moderate COPD

Fibrinogen is a class II acute phase reactant and is solely regulated by IL-6 and glucocorticoids (9) Although it has been suggested that fibrinogen may predict prognosis (6) and be part of a specific inflammatory phenotype in COPD (10), it is worth noting that previous studies did not exclude patients with cardiovascular disorders, in whom fibrinogen levels are notoriously increased (24) Because this potential confounder has been removed from our study, we believe that future research should reevaluate the potential role of fibrinogen in COPD However, we have found an association between fibrinogen and tPA in advanced COPD (GOLD IV); both molecules participate in the coagulation cascade Fibrinogen is implicated in clot formation whereas tPA plays a role in its dissolution, with both of these phenomena being present in patients with advanced disease

Figure 4 Distinct networks of acute phase reactants according to the severity of COPD Panel A: patients with GOLD I COPD; panel B: patients with GOLD II

COPD; panel C: patients with GOLD III COPD; panel D: patients with GOLD IV COPD Node colors indicate the type of correlation (grey and black denote positive and negative correlations, respectively), whereas node size is proportional to the extent of correlation

Among the inflammatory networks of COPD,

network analysis revealed a prominent association

between PCT and tPA PCT is the precursor of

calcitonin, whose serum levels have been studied in

patients with respiratory infections as a marker for

guiding antibiotic therapy during exacerbations (25)

Although a recent report demonstrated a correlation

of salivary PCT levels with both breathing scores and

sputum features in COPD (26), to our knowledge the

potential impact of plasma PCT in patients with stable

disease has not been previously investigated

However, its potential role as a biomarker is worth of

investigation in future studies

The present study must be evaluated in the light

of several limitations In the current report, we limited

our analysis to male subjects Different studies have

shown that sex may have a significant impact in the

clinical presentation of COPD from both the clinical

(21) and inflammatory (22, 23) standpoints Further

research is needed to investigate whether our current

findings may be applied to females as well The

severity of COPD was evaluated with FEV1 alone

However, we know that the evaluation of severity can

be more complex and comprehensive In this regard,

future studies can evaluate the impact of the

inflammatory load on different clinical features

Finally, other non-cardiac comorbidities may also

influence the results, since several chronic diseases

have been associated with elevated systemic

biomarkers, although we did not find any association

between biomarkers and comorbidities

In summary, we performed an integrative

statistical analysis of different inflammatory markers

in COPD after the exclusion of patients with ischemic

heart disease Importantly, we were able to identify

different networks of APRs which were significantly

associated with COPD severity Pending external

validation, the markers or signatures herein identified

could be helpful for patient monitoring, stratification

in clinical trials, or personalizing existing or

upcoming anti-inflammatory therapies

Competing Interests

The authors have declared that no competing

interest exists

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